The present invention relates to an abstractor having a width of 1 bit, an abstractor having a width of N bits, the use of an abstractor having a width of N bits and to a neural network employing an abstractor having a width of N bits.
In technical as well as biological systems, the transmission of data is subject to interferences. One possibility of ensuring the correct transmission of the data in spite of interferences is to repeat the data to be transmitted (hereinafter called the structural unit) several times at the transmitting end, for example the message
1111 1111 1111 . . . , PA0 0111 1101 1001 . . . ,
and at the receiving end to reconstruct this message from the repetitions (hereinafter called singulars) arriving there which are interfered with to a greater or lesser extent, for example,
by utilizing their redundancy. This reconstruction is performed by an abstractor which is the object of the invention.
A paradigm for the use of an abstractor is the simulation of visual perception. The eyes are either fixed or moving; as long as they are fixed, they record a sequence of similar images; during movement, a "dark phase" exists in which nothing is perceived. The eye thus furnishes sequences of images of similar contents; a sequence of empty noise images picked up during eye movement lies between every two such sequences. It is not known when the change to a new image sequence takes place; to determine when it happens, is part of the reconstruction task.
The singulars are similar to one another; however, they are also similar to the structural unit from which they evolved due to the superposition of interferences. Similarity means: there are "features" which distinguish the individual singulars from one another and from their structural parent and those which they all have in common. The abstraction process resides in abstracting those "features" in which the singulars differ; these are the locations in particular which were falsified by the interference on the transmission path. If one therefore considers the structural unit as an exemplary pattern of a class of patterns and the associated singular as realizations of this class of patterns, the abstraction process can be understood as the generation of this exemplary pattern from a certain number of examples.
The generation of exemplary patterns from examples has been attempted, among others, with neural networks, for example with a Carpenter-Grossberg classifier. The neural networks known in the art are based on the principle of a weighted spread of activity. They differ from one another in their network topology, their training rules and the characteristics of their nodes (artificial neurons).
Neural networks used as classifiers associate iteratively an unknown pattern to one of the exemplary patterns they have been trained to. Prerequisite for this, however, is the convergence of the iteration process. The networks can be distinguished as those in which the exemplary patterns must be known in advance (such as, for example, in the Hopfield or Hamming networks)--training here corresponds to supervised learning--and those in which the exemplary patterns are formed simultaneously with the training (such as, for example, in a Carpenter-Grossberg network); in this case, unsupervised learning takes place ("An Introduction to Computing with Neural Nets" by R. P. Lippmann, IEEE ASSP Magazine, April, 1987, pages 4-22, "Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns" by G. Carpenter, S. Grossberg, Applied Optics, Dec. 1, 1987, Vol. 26, No. 23, pages 4919-4930).
Only the neural networks of the latter type can be compared with an abstractor. However, they have considerable drawbacks which make them appear unsuitable for use: they are susceptible to interferences; with a given error rate, the convergence of the prior art methods cannot be insured; they are very computation intensive; their use is limited to a defined number of exemplary patterns.